Data Analyst Tutorial with Python | Iris Dataset Project for Beginners (EDA + Logistic Regression) скачать в хорошем качестве

Data Analyst Tutorial with Python | Iris Dataset Project for Beginners (EDA + Logistic Regression)

Learn how to become a Data Analyst by working on the Iris Dataset using Python. This step-by-step tutorial includes data importing, preparation, exploratory data analysis (EDA), and logistic regression model building. Perfect for beginners looking to improve their data science and analytics skills. In this tutorial, you’ll cover: Importing and loading datasets Visualizing data with bar charts, box plots, pair plots, and heatmaps Splitting data and building predictive models Applying logistic regression Evaluating the model with confusion matrix Subscribe for more data analytics and machine learning tutorials! ---------------------------------------------------------- 00:04 - Import Dataset Iris, pandas read_csv, load iris.csv file 00:20 - Load Dataset in Pandas, view dataframe 00:30 - About Iris Dataset, features and target variable explained 01:35 - Data Preparation, cleaning and formatting data 01:55 - EDA Bar Chart, visualize class distribution 02:29 - EDA Box Plots, detect outliers and variance 03:11 - EDA Pair Plot, seaborn pairplot for feature relationships 03:34 - EDA Correlation Matrix, feature correlation in iris dataset 03:55 - EDA Heatmap Visualization, seaborn heatmap correlation 04:11 - Model Building, preparing data for ML 04:24 - Train the Model, training logistic regression 04:42 - Split the Data, train-test split using sklearn 04:58 - Apply Logistic Regression, sklearn LogisticRegression model 05:12 - Confusion Matrix Evaluation, performance metrics for classification